Existing computer systems provide little appreciation of a user's overall condition or context, and as a result they can effectively respond to only a limited number of changes in parameters that they monitor. For example, with respect to the low-level physical status of the user, numerous devices exist for monitoring the physical parameters of the user, such as heart rate monitors that provide user pulse or heart rate data. While many of these devices simply provide information to the user regarding current values of a user's health condition, others (e.g., a defibrillator or a system with an alarm) are capable of providing a corresponding response if a monitored parameter exceeds (or falls below) a threshold value. However, since such devices lack important information about the specific context of the user (e.g., whether the user is currently exercising or is currently sick), any response will attempt to accommodate a wide range of user contexts and is thus unlikely to be optimal for the specific context of the user. For example, a defibrillator may provide too great or too small of a resuscitating charge simply because only one or a small number of parameters of a person are being monitored.
In a similar manner, existing computer systems have little appreciation for a user's current mental and emotional state, or for higher-level abstractions of a user's physical activity (e.g., going jogging or driving an automobile), and as a result are generally ineffective at anticipating tasks that a user is likely to perform or information that a user is likely to desire. In particular, since existing computer systems lack information about a user's current context, they cannot provide information appropriate to that context or anticipate likely changes in the context.